Meeting the demand for granular and explainable data requires transitioning from tactical reporting fixes to integrated, intelligent architecture, participants heard at Regnology's RegInsights event.

Regulatory reporting is no longer just a question of whether banks can submit the right template by the deadline. As supervisors across APAC demand more granular, timely and explainable data, the harder test is whether institutions can reconstruct, explain and evidence the numbers they report.

Singapore’s regulatory landscape reflects this broader global trend, shifting focus away from template-based reporting towards the data and technology foundations needed to ensure reported figures are traceable, auditable and trusted.

That was a key takeaway from Regnology's RegInsights event in Singapore earlier this summer. Across sessions on regulatory updates, data management and technology innovation, a central message emerged: meeting evolving regulatory expectations requires moving beyond isolated reporting processes towards an integrated operating model built on trusted data, technology and governance.

Regnology will advance the industry dialogue in November at the 33rd RegTech Convention under the theme "From Control to Intelligence," focusing on activating data as an engine of insight, unlocking intelligence across reporting, risk, and finance.

A convergence of regulatory pressures

This shift isn't the result of a single policy change, but a convergence of several forces. Prudential requirements are becoming more complex and interconnected, requiring institutions to assess impacts across capital, risk and reporting frameworks simultaneously.

In Singapore, developments such as the Basel III output floor under MAS Notice 637, revised large exposure requirements under MAS 656 and emerging crypto-asset frameworks illustrate the breadth of change underway. While each regulation addresses a different risk, the common thread is a growing need for faster recalculation, greater transparency and the ability to explain reported outcomes on demand.

For institutions operating in multiple jurisdictions, the challenge is amplified. While regulatory objectives are increasingly aligned at a global level, implementation timelines, reporting requirements and supervisory expectations still vary across markets. Maintaining consistency in data, controls and governance across jurisdictions is becoming progressively more difficult.

Supervisory expectations are also evolving. Regulators are increasingly leveraging modern infrastructure, supervisory technology (SupTech), and advanced analytics to interrogate reported data for effective oversight. For financial institutions, this raises the evidentiary bar – making data quality, end-to-end lineage, and explainable, auditable reporting essential.

The fragility of old architectures

This new reality exposes a key vulnerability in legacy operating models. For decades, many institutions have treated each new reporting requirement as an isolated, tactical project. This led to an array of point-to-point fixes and manual workarounds, resulting in fragmented processes, redundant spreadsheets, and operational complexities that are difficult to manage, scale, or adapt.

As one regional banking executive, recalling the extensive remapping efforts during the last major reporting overhaul under MAS 610, noted, the primary concern today is not the sheer volume of data. Instead, it is the level of assurance required to prove that the data is accurate, complete, and timely. The traditional approach of pulling data into a reporting engine at the last minute is becoming unsustainable, particularly as regulators demand greater transparency around data lineage and reporting outcomes.

'Map once, report many'

Event discussions highlighted the importance of building robust data foundations capable of supporting multiple reporting requirements and future regulatory change.

The most sustainable path forward lies in a fundamental shift in philosophy, described by Regnology experts as "map once, report many.” This approach reframes data architecture from a compliance cost center into a strategic enterprise asset. The goal is to build a trusted data foundation – a single source of truth – that simultaneously serves the needs of risk management, finance, and regulatory reporting.

In practice, this separates data sourcing and standardization from the downstream reporting logic. By establishing an integrated, standardized data structure, institutions no longer need to scramble for data when new regulations arrive; compliance becomes a straightforward configuration of reporting rules atop a reliable foundation.

While requiring an initial investment, this architecture delivers long-term resilience and agility. As supervisors across Singapore, Hong Kong, Malaysia, and Australia move toward more granular, transaction-level data, a robust data foundation is rapidly becoming the regional benchmark for scalable compliance.

Built on the Regnology Granular Data (RGD) Model, the cloud-native Regnology Reporting Hub (RRH) was presented as one example of how institutions can operationalize this ‘map once, report many’ approach.

Modern, cloud-native architecture and data foundations have emerged as essential building blocks for scalable reporting and AI adoption. Unifying robust data management, governance and end-to-end workflows within a secure and trusted environment enables intelligent automation and accelerates reporting cycles.

Pragmatic AI: The governed co-pilot

The conversation around artificial intelligence (AI) has moved past the initial hype toward practical, governed implementation. The prevailing consensus aligns with regulatory expectations: a “human-in-the-loop" model.

Embedded within governed reporting workflows, AI acts as an intelligent co-pilot – scanning portfolio data, identifying anomalies, explaining liquidity metric variances and suggesting actions. Critically, accountability and final decisions remain strictly with human experts. Every proposal, action, and approval is logged in an immutable audit trail, creating the clear, explainable record that meets regulatory demands.

A recurring theme throughout the event was the move towards governed intelligence. This was reflected in Ascend, Regnology's cloud-first, agentic-first platform, and its Governance Intelligence Layer (RGI), which enables supervised and explainable agent behavior across reporting workflows, ensuring traceability, auditability and human-in-the-loop oversight.

Convergence, not distinction

This combination of regulatory pressure, technology innovation, and architectural necessity is driving a broader shift across the industry.

As Regnology’s General Manager APAC, Philipp Hühne, aptly put it: “Regulatory reporting is the only area where institutions don’t want to be special. You want to move in a pack.”

In this new paradigm, being an outlier, a bank that can't explain a variance or trace its data lineage, invites a level of scrutiny that institutions wish to avoid.

Reporting templates, for their part, are not disappearing. But their role has fundamentally changed. Rather than serving as the foundation of the reporting process, they are simply the final output of an integrated operating model.

Over the next few years, the true differentiator between financial institutions will not be the submission itself, but the robustness and agility of the operating model behind it. Success will increasingly depend on how effectively institutions connect data, technology, and governance to establish a reporting foundation that is trusted, scalable and adaptable to change.

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This article was produced through a collaboration between Regnology and Regulation Asia.